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基于模式相关性的手掌纹和手掌静脉的融合识别
Wei Wu1, Yuan Zhang1, Yunpeng Li1
1School of Information Engineering, Shenyang University, Shenyang 110044, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究引入了一种新的特征级联合学习融合方法,用于手掌纹和手掌静脉生物识别. 该方法通过关联各个模式的特征来提高安全性和准确性,大大提高了身份验证的性能.
科学领域:
- 生物识别和人工智能的人工智能
- 模式识别 模式识别
- 机器学习 机器学习
背景情况:
- 在AI时代,生物识别身份验证对于防止身份滥用至关重要.
- 多模式生物识别,如手掌纹和手掌静脉融合,提供了增强的安全性和效率.
- 现有的方法往往忽略了模式间的相关性,限制了识别性能.
研究的目的:
- 为掌纹和手掌静脉识别提出一个特征级联合学习融合方法.
- 为了提高准确性和防伪能力,利用模式相关性.
- 为了解决现有方法的局限性,这些方法可以独立提取特征.
主要方法:
- 一个稀疏的无监督投影算法与"净化矩阵"被用于内部模式的特征一致性.
- 部分最小平方 (PLS) 从每个模式中提取了与类别相关的子空间.
- 一种加权总和方法动态优化了每个模式对分类的贡献.
主要成果:
- 对五个多式联络数据库和六个单式联络数据库进行了实验性评估.
- 实现了极具竞争力的相同错误率 (EER),低至0.0008%.
- 与传统的核聚变方法相比,表现出显著的性能改进.
结论:
- 拟议的联合学习融合方法有效地利用模态相关性,以获得卓越的手掌纹和手掌静脉识别.
- 该方法提高了准确性和稳定性,使其适用于高安全性身份识别场景.
- 这种方法对于先进的生物识别系统具有实际价值.
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